Aim: To present the translation and validation process of the Portuguese version of the Tilburg Frailty Indicator (TFI). Methods:A cross-sectional study was designed using a non-probability sample of 252 community-dwelling older adults. Preliminary studies were carried out for face and content validity assessment. Internal consistency, test-retest reliability, construct (convergent/divergent) and criterion validity were subsequently analyzed. Results:The sample was mainly women (75.8%), with a mean age of 79.2 ± 7.3 years. TFI internal consistency was good (KR-20 = 0.78). Test-retest reliability for the total was also good (r = 0.91), with kappa coefficients showing substantial agreement for most items. TFI physical and social domains correlated as expected with concurrent measures, whereas the TFI psychological domain showed similar correlations with other psychological and physical measures. The TFI showed a good to excellent discrimination ability in regard to frailty criteria, and fair to good ability to predict adverse outcomes. Conclusions:The psychometric properties of the TFI seem to be consistently good. These findings provide initial evidence that the Portuguese version is a valid and reliable measure for assessing frailty in the elderly.

This study aims to analyze which determinants predict frailty in general and each frailty domain (physical, psychological, and social), considering the integral conceptual model of frailty, and particularly to examine the contribution of medication in this prediction. A cross-sectional study was designed using a non-probabilistic sample of 252 community-dwelling elderly from three Portuguese cities. Frailty and determinants of frailty were assessed with the Tilburg Frailty Indicator. The amount and type of different daily-consumed medication were also examined. Hierarchical regression analysis were conducted. The mean age of the participants was 79.2 years (±7.3), and most of them were women (75.8%), widowed (55.6%) and with a low educational level (0–4 years: 63.9%). In this study, determinants explained 46% of the variance of total frailty, and 39.8, 25.3, and 27.7% of physical, psychological, and social frailty respectively. Age, gender, income, death of a loved one in the past year, lifestyle, satisfaction with living environment and self-reported comorbidity predicted total frailty, while each frailty domain was associated with a different set of determinants. The number of daily-consumed drugs was independently associated with physical frailty, and the consumption of medication for the cardiovascular system and for the blood and blood-forming organs explained part of the variance of total and physical frailty. The adverse effects of polymedication and its direct link with the level of comorbidities could explain the independent contribution of the amount of prescribed drugs to frailty prediction. On the other hand, findings in regard to medication type provide further evidence of the association of frailty with cardiovascular risk. In the present study, a significant part of frailty was predicted, and the different contributions of each determinant to frailty domains highlight the relevance of the integral model of frailty. The added value of a simple assessment of medication was considerable, and it should be taken into account for effective identification of frailty.

The objectives of this study were to compare how different frailty measures (Frailty Phenotype/FP, Groningen Frailty Indicator/GFI and Tilburg Frailty Indicator/TFI) predict short-term adverse outcomes. Secondarily, adopting a multidimensional approach to frailty (integral conceptual model–TFI), this study aims to compare how physical, psychological and social frailty predict the outcomes. A longitudinal study was carried out with 95 community-dwelling elderly. Participants were assessed at baseline for frailty, determinants of frailty, and adverse outcomes (healthcare utilization, quality of life, disability in basic and instrumental activities of daily living/ADL and IADL). Ten months later the outcomes were assessed again. Frailty was associated with specific healthcare utilization indicators: the FP with a greater utilization of informal care; GFI with an increased contact with healthcare professionals; and TFI with a higher amount of contacts with a general practitioner. After controlling for the effect of life-course determinants, comorbidity and adverse outcome at baseline, GFI predicted IADL disability and TFI predicted quality of life. The effect of the FP on the outcomes was not significant, when compared with the other measures. However, when comparing TFI’s domains, the physical domain was the most significant predictor of the outcomes, even explaining part of the variance of ADL disability. Frailty at baseline was associated with adverse outcomes at follow-up. However, the relationship of each frailty measure (FP, GFI and TFI) with the outcomes was different. In spite of the role of psychological frailty, TFI’s physical domain was the determinant factor for predicting disability and most of the quality of life.

This study aimed to examine the differences in standing balance between individuals with Parkinson's disease (PD) and subjects without PD (control group), under single and dual-task conditions. A cross-sectional study was designed using a non-probabilistic sample of 110 individuals (50 participants with PD and 60 controls) aged 50 years old and over. The individuals with PD were in the early or middle stages of the disease (characterized by Hoehn and Yahr as stages 1-3). The standing balance was assessed by measuring the centre of pressure (CoP) displacement in single-task (eyes-open/eyes-closed) and dual-task (while performing two different verbal fluency tasks). No significant differences were found between the groups regarding sociodemographic variables. In general, the standing balance of the individuals with PD was worse than the controls, as the CoP displacement across tasks was significantly higher for the individuals with PD (p<0.01), both in anteroposterior and mediolateral directions. Moreover, there were significant differences in the CoP displacement based parameters between the conditions, mainly between the eyes-open condition and the remaining conditions. However, there was no significant interaction found between group and condition, which suggests that changes in the CoP displacement between tasks were not influenced by having PD. In conclusion, this study shows that, although individuals with PD had a worse overall standing balance than individuals without the disease, the impact of performing an additional task on the CoP displacement is similar for both groups.

This study aimed to explore the feasibility and effects of promoting reminiscences, using virtual reality (VR) headsets for viewing 360° videos with personal relevance, with people with dementia. A study with a mixed methods design was conducted with nine older adults diagnosed with dementia. Interventions consisted of four sessions, in which the participants’ engagement, psychological and behavioral symptoms, and simulation sickness symptoms were evaluated. Neuropsychiatric symptomatology and quality of life were measured pre- and post-intervention. Caregivers were interviewed regarding the effect of the approach. In most cases, participants appeared to enjoy the sessions, actively explored the 360° environment, and shared memories associated with the depicted locations, often spontaneously. There were no cases of significant increases in simulator sickness and psychological and behavioral symptoms during sessions, with only some instances of minor eyestrain, fullness of head, anxiety, irritability, and agitation being detected. Although there were no significant changes in the measured outcomes after intervention, the caregivers assessed the experience as potentially beneficial for most participants. In this study, promoting reminiscences with VR headsets was found to be a safe and engaging experience for people with dementia. However, future studies are required to better understand the added value of immersion, using VR, in reminiscence therapy.

Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain activity and the seizure discharge. This period is typically a fixed interval, with some recent studies reporting the evaluation of different patient-specific pre-ictal intervals. Recently, researchers have aimed to determine the pre-ictal period, a transition stage between regular brain activity and a seizure. Authors have been using deep learning models given the ability of such models to automatically perform pre-processing, feature extraction, classification, and handling temporal and spatial dependencies. As these approaches create black-box models, clinicians may not have sufficient trust to use them in high-stake decisions. By considering these problems, we developed an evolutionary seizure prediction model that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort. This methodology provides patient-specific interpretable insights, which might contribute to a better understanding of seizure generation processes and explain the algorithm’s decisions. We tested our methodology on 238 seizures and 3687 h of continuous data, recorded on scalp recordings from 93 patients with several types of focal and generalised epilepsies. We compared the results with a seizure surrogate predictor and obtained a performance above chance for 32% patients. We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients. In total, 54 patients performed above chance for at least one method: our methodology or the control one. Of these 54 patients, 21 ($$\approx$$ ≈ 38%) were solely validated by our methodology, while 24 ($$\approx$$ ≈ 44%) were only validated by the control method. These findings may evidence the need for different methodologies concerning different patients.

Frailty was independently predicted by pain, emphasizing the importance of its treatment, potentially contributing to the prevention of vulnerability, dependency, and mortality. Nonetheless, longitudinal studies are required to better understand the possible association between pain and frailty.

Social components of frailty vary from instrument to instrument and cover the concepts of social isolation, loneliness, social network, social support and social participation.